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Oriented wavelet transform for image compression and denoising.

Vivien Chappelier1, Christine Guillemot

  • 1IRISA/University of Rennes 1, France. vivien.chappelier@free.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 7, 2006
PubMed
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This study presents a novel wavelet-based image processing transform using adaptive local orientations for enhanced energy compaction. It shows improved performance in image compression and denoising applications.

Area of Science:

  • Image Processing
  • Signal Analysis
  • Computer Vision

Background:

  • Traditional wavelet transforms offer multiscale analysis but can lack directional adaptivity.
  • The lifting paradigm enables efficient and reversible wavelet construction.
  • Existing methods for image compression and denoising have limitations in capturing local image structures.

Purpose of the Study:

  • Introduce a new image processing transform leveraging wavelets and the lifting paradigm.
  • Enhance energy compaction and adaptivity for improved image coding and denoising.
  • Investigate the properties and performance of the proposed transform against existing methods.

Main Methods:

  • Applied unidimensional wavelet lifting steps along adaptively chosen local orientations on a quincunx grid.

Related Experiment Videos

  • Iterated decomposition for fine-grained multiscale analysis.
  • Utilized quad-tree coding for the orientation map and joint rate-distortion optimization for image compression.
  • Employed a Markov model for orientation extraction in image denoising.

    • Main Results:

    • Achieved adaptive orientation selection to minimize prediction error, maximizing energy compaction.
    • Demonstrated preserved wavelet properties like regularity and orthogonality due to the lifting scheme.
    • Evaluated superior rate-distortion performance in image coding and effective denoising capabilities compared to other transforms.

    Conclusions:

    • The novel wavelet transform offers significant advantages for image processing tasks.
    • Adaptive orientation selection and the lifting scheme contribute to high performance in compression and denoising.
    • This transform provides a powerful new tool for advanced image analysis and manipulation.